Working with the audio production and engineering industry, I often wonder how the future of the voice talent market will look like with the assistance of artificial intelligence. The development of cloning technology started a while back but it did not reach today's level overnight. The debate of misusing this…

Artificial Intelligence Overview

AI refers to ‘Artificial Intelligence’ which means making machines capable of performing intelligent tasks like human beings. AI performs automated tasks using intelligence.

The term Artificial Intelligence has two key components -

• Automation  
• Intelligence

For More…

Text classification (a.k.a. text categorization) is one of the most prominent application of Machine Learning. The purpose of text classification is to give conceptual organization to large collection of documents.An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for…

The global text analytics market has been showing tremendous growth over the last few years. The increasing need for social media analytics and the rising number of applications associated with industry-specific text analytics are driving the demand for text analytics solution across the world significantly.

In 2015, the worldwide market for text analytics stood at US$2.8 bn in terms of revenue. Rising at a CAGR of 17.60% between 2016 and 2024, the market is expected to reach a value…

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the…

This blog post was originally published as part of an ongoing series, "Popular Algorithms Explained in Simple English" on the AYLIEN Text Analysis Blog.

Picture added by the…

Let's say a set of documents 'S' has a large set of 'pure' texts.

On all documents in S, I am spelling normalisation method, which yields a normalised set S'.

Then I use the chosen method M (which method? ) to make clusters in S, obtaining a clustering result C.

Then I use the same method M to make clusters in S', obtaining a clustering results C'.

Finally I need to compare if there are statistically significant differences between C and C'.

Any help in identifying…

Please join us in New York at the Sentiment Analysis Symposium Customer Insight Analytics Workshop on March 5 where Steven Ramirez, CEO of Beyond the Arc, will be presenting The Road to Customer Intelligence: Data, Analytics, Insight.

The afternoon workshop will offer a thorough, practical look at elements that business analysts, managers, and executives must master to compete in today’s…

 In a recent interview with CIO Review magazine, Beyond the Arc CEO, Steven Ramirez shared his insights on the evolution of Big Data analytics in 2013, and what we can expect in 2014. 

Social media and interplanetary mission -- what do they have in common? Well, they have in common the Mars Orbiter Mission, also known as ‘Mangalyaan’. It was launched on 5th November by the Indian Space Research Organization (ISRO). It generated a lot of interest across the globe among millions of people on Social Media networks. In this blog, we analyze how Twitterati reacted to this news.



Asia was by far the most interested in the subject, covering 74% of all…

Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like "Bag of word", "Clustering" and "vectorization". Vectorization is  must-to-know technique for all machine leaning learners, text miner…

There is no question that the USA (in fact, most of the world) would be well-served with more quantitatively capable people to work in business and government. However, the current hysteria over the shortage of data scientists is overblown. To illustrate why, I am going to use an example from air travel.

On a recent trip from Santa Fe, NM to Phoenix, AZ, I tracked the various times: